{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Datasets from the NormA journal paper(WADI)\n",
    "\n",
    "- Paper: https://doi.org/10.1007/s00778-021-00655-8\n",
    "- Website: https://helios2.mi.parisdescartes.fr/~themisp/norma/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "\n",
    "from timeeval import Datasets, DatasetManager\n",
    "from timeeval.datasets import DatasetAnalyzer, DatasetRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/NormA/selected_datasets and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "source_folder = Path(data_raw_folder) / \"NormA\" / \"selected_datasets\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "print(f\"Looking for source datasets in {Path(source_folder).absolute()} and\\nsaving processed datasets in {Path(target_folder).absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created directories /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA\n"
     ]
    }
   ],
   "source": [
    "# shared by all datasets\n",
    "dataset_collection_name = \"NormA\"\n",
    "input_type = \"univariate\"\n",
    "datetime_index = False\n",
    "split_at = None\n",
    "train_is_normal = False\n",
    "train_type = \"unsupervised\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "target_subfolder = target_folder / dataset_subfolder\n",
    "target_subfolder.mkdir(parents=True, exist_ok=True)\n",
    "print(f\"Created directories {target_subfolder}\")\n",
    "\n",
    "dm = DatasetManager(target_folder)\n",
    "\n",
    "# load discord information for real datasets\n",
    "ANOMALY_INFORMATION = \"anomaly_information.csv\"\n",
    "df_anomaly = pd.read_csv(source_folder / ANOMALY_INFORMATION)\n",
    "df_anomaly = df_anomaly.set_index(\"dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filepath: Path, dataset_name: str, dataset_type: str) -> pd.DataFrame:\n",
    "    if dataset_type == \"synthetic\":\n",
    "        discord_positions = np.genfromtxt(filepath.parent / f\"{dataset_name}_Annotations.txt\", delimiter=\",\")\n",
    "        discord_length = int(filepath.stem.split(\"_\")[4].strip())\n",
    "        length = None\n",
    "    else:\n",
    "        discord_positions = df_anomaly.loc[dataset_name, \"anomaly_indices\"].split(\";\")\n",
    "        discord_positions = np.array([int(i) for i in discord_positions])\n",
    "        discord_length = df_anomaly.loc[dataset_name, \"anomaly_length\"]\n",
    "        length = df_anomaly.loc[dataset_name, \"length\"]\n",
    "    anomalies = []\n",
    "    for idx in discord_positions:\n",
    "        anomalies.append(np.arange(idx, idx+discord_length))\n",
    "\n",
    "    df = pd.read_csv(filepath, header=None)\n",
    "    df.index.name = \"timestamp\"\n",
    "    df.columns = [\"value\"]\n",
    "    if length is not None:\n",
    "        df = df.iloc[:length]\n",
    "\n",
    "    # insert labels\n",
    "    df[\"is_anomaly\"] = 0\n",
    "    for anomaly in anomalies:\n",
    "        df.loc[anomaly, \"is_anomaly\"] = 1\n",
    "\n",
    "    df = df.reset_index()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n",
      "[('NormA', 'Discords_patient_respiration2') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_patient_respiration2.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60_NoisePerc_0\n",
      "Processing dataset Discords_patient_respiration2\n",
      "  written test dataset\n",
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_patient_respiration2\n",
      "Processing dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_10\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_10') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_10.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_10\n",
      "Processing dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_20\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_20') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_20.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_20\n",
      "Processing dataset SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_NoisePerc_0\n",
      "Processing dataset Discords_marotta_valve_tek_17\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'Discords_marotta_valve_tek_17') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_marotta_valve_tek_17.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_marotta_valve_tek_17\n",
      "Processing dataset SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_NoisePerc_0\n",
      "Processing dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_15\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_15') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_15.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_15\n",
      "Processing dataset SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_NoisePerc_0\n",
      "Processing dataset SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_NoisePerc_0\n",
      "Processing dataset SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_NoisePerc_0\n",
      "Processing dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_5\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_5') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_5.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_5\n",
      "Processing dataset SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n",
      "[('NormA', 'Discords_annsgun') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_annsgun.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_NoisePerc_0\n",
      "Processing dataset Discords_annsgun\n",
      "  written test dataset\n",
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_annsgun\n",
      "Processing dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_25\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_25') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_25.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_25\n",
      "Processing dataset SinusRW_Length_120000_AnomalyL_200_AnomalyN_100_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_120000_AnomalyL_200_AnomalyN_100_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_120000_AnomalyL_200_AnomalyN_100_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_120000_AnomalyL_200_AnomalyN_100_NoisePerc_0\n",
      "Processing dataset Discords_patient_respiration1\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'Discords_patient_respiration1') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_patient_respiration1.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_patient_respiration1\n",
      "Processing dataset Discords_marotta_valve_tek_16\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'Discords_marotta_valve_tek_16') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_marotta_valve_tek_16.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_marotta_valve_tek_16\n",
      "Processing dataset Discords_marotta_valve_tek_14\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'Discords_marotta_valve_tek_14') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_marotta_valve_tek_14.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_marotta_valve_tek_14\n",
      "Processing dataset Discords_dutch_power_demand\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'Discords_dutch_power_demand') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_dutch_power_demand.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset Discords_dutch_power_demand\n",
      "Processing dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_0\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('NormA', 'SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_0') (test)] /home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_0.metadata.json already exists, but 'overwrite' was specified! Ignoring existing contents.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed and written test dataset metadata\n",
      "... processed dataset SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_0\n"
     ]
    }
   ],
   "source": [
    "time_series_paths = [f for f in source_folder.glob(\"*.ts\") if f.stem != ANOMALY_INFORMATION.split(\".\")[0]]\n",
    "for filepath in time_series_paths:\n",
    "    dataset_name = filepath.stem\n",
    "    print(f\"Processing dataset {dataset_name}\")\n",
    "    \n",
    "    test_filename = f\"{dataset_name}.test.csv\"\n",
    "    test_path = dataset_subfolder / test_filename\n",
    "    target_test_filepath = target_subfolder / test_filename\n",
    "    target_meta_filepath = target_subfolder / f\"{dataset_name}.metadata.json\"\n",
    "\n",
    "    dataset_type = \"real\"\n",
    "    if dataset_name.startswith(\"SinusRW\"):\n",
    "        dataset_type = \"synthetic\"\n",
    "\n",
    "    # prepare test dataset\n",
    "    df_test = prepare_dataset(filepath, dataset_name, dataset_type)\n",
    "    df_test.to_csv(target_test_filepath, index=False)\n",
    "    print(\"  written test dataset\")\n",
    "\n",
    "    # analyze test dataset\n",
    "    da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df_test)\n",
    "    meta = da.metadata\n",
    "    da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "    print(\"  analyzed and written test dataset metadata\")\n",
    "\n",
    "    dm.add_dataset(DatasetRecord(\n",
    "          collection_name=dataset_collection_name,\n",
    "          dataset_name=dataset_name,\n",
    "          train_path=None,\n",
    "          test_path=test_path,\n",
    "          dataset_type=dataset_type,\n",
    "          datetime_index=datetime_index,\n",
    "          split_at=split_at,\n",
    "          train_type=train_type,\n",
    "          train_is_normal=train_is_normal,\n",
    "          input_type=input_type,\n",
    "          length=meta.length,\n",
    "          dimensions=meta.dimensions,\n",
    "          contamination=meta.contamination,\n",
    "          num_anomalies=meta.num_anomalies,\n",
    "          min_anomaly_length=meta.anomaly_length.min,\n",
    "          median_anomaly_length=meta.anomaly_length.median,\n",
    "          max_anomaly_length=meta.anomaly_length.max,\n",
    "          mean=meta.mean,\n",
    "          stddev=meta.stddev,\n",
    "          trend=meta.trend,\n",
    "          stationarity=meta.get_stationarity_name(),\n",
    "          period_size=np.nan\n",
    "    ))\n",
    "    print(f\"... processed dataset {dataset_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>contamination</th>\n",
       "      <th>num_anomalies</th>\n",
       "      <th>min_anomaly_length</th>\n",
       "      <th>median_anomaly_length</th>\n",
       "      <th>max_anomaly_length</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">NormA</th>\n",
       "      <th>Discords_annsgun</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_annsgun.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>3.097742e+02</td>\n",
       "      <td>96.244237</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_dutch_power_demand</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_dutch_power_demand.t...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>35040</td>\n",
       "      <td>1</td>\n",
       "      <td>0.091324</td>\n",
       "      <td>4</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>1.144038e+03</td>\n",
       "      <td>292.725791</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_marotta_valve_tek_14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_marotta_valve_tek_14...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.051200</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>1.120064e+00</td>\n",
       "      <td>1.703123</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_marotta_valve_tek_16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_marotta_valve_tek_16...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025600</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>1.054768e+00</td>\n",
       "      <td>1.665258</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_marotta_valve_tek_17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_marotta_valve_tek_17...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025600</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>9.959120e-01</td>\n",
       "      <td>1.626007</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_patient_respiration1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_patient_respiration1...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>6500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>7.452095e+01</td>\n",
       "      <td>289.473775</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_patient_respiration2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_patient_respiration2...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>7.977460e+01</td>\n",
       "      <td>77.999132</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_104000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>104000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-6.943642e-15</td>\n",
       "      <td>1.413004</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_106000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>106000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>60</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-5.439808e-15</td>\n",
       "      <td>1.414737</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_108000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>108000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>40</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-1.370558e-15</td>\n",
       "      <td>1.412455</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>9.062465e-15</td>\n",
       "      <td>1.413815</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>5.083426e-15</td>\n",
       "      <td>1.411193</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-1.226194e-14</td>\n",
       "      <td>1.412934</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-4.125208e-15</td>\n",
       "      <td>1.413281</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-8.619898e-15</td>\n",
       "      <td>1.410894</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-8.522453e-15</td>\n",
       "      <td>1.411797</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_116000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>116000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>80</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-7.126989e-15</td>\n",
       "      <td>1.413798</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_120000_AnomalyL_200_AnomalyN_100_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_120000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>120000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-4.704740e-15</td>\n",
       "      <td>1.413511</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_124000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>124000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>60</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>3.163061e-15</td>\n",
       "      <td>1.414285</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_148000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>148000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>60</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>-7.988805e-17</td>\n",
       "      <td>1.414400</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60_NoisePerc_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_196000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>196000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.489796</td>\n",
       "      <td>60</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>-2.561434e-15</td>\n",
       "      <td>1.411909</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   train_path  \\\n",
       "collection_name dataset_name                                                    \n",
       "NormA           Discords_annsgun                                          NaN   \n",
       "                Discords_dutch_power_demand                               NaN   \n",
       "                Discords_marotta_valve_tek_14                             NaN   \n",
       "                Discords_marotta_valve_tek_16                             NaN   \n",
       "                Discords_marotta_valve_tek_17                             NaN   \n",
       "                Discords_patient_respiration1                             NaN   \n",
       "                Discords_patient_respiration2                             NaN   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...        NaN   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...        NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...        NaN   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...        NaN   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...        NaN   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...        NaN   \n",
       "\n",
       "                                                                                                            test_path  \\\n",
       "collection_name dataset_name                                                                                            \n",
       "NormA           Discords_annsgun                                           univariate/NormA/Discords_annsgun.test.csv   \n",
       "                Discords_dutch_power_demand                         univariate/NormA/Discords_dutch_power_demand.t...   \n",
       "                Discords_marotta_valve_tek_14                       univariate/NormA/Discords_marotta_valve_tek_14...   \n",
       "                Discords_marotta_valve_tek_16                       univariate/NormA/Discords_marotta_valve_tek_16...   \n",
       "                Discords_marotta_valve_tek_17                       univariate/NormA/Discords_marotta_valve_tek_17...   \n",
       "                Discords_patient_respiration1                       univariate/NormA/Discords_patient_respiration1...   \n",
       "                Discords_patient_respiration2                       univariate/NormA/Discords_patient_respiration2...   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...  univariate/NormA/SinusRW_Length_104000_Anomaly...   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...  univariate/NormA/SinusRW_Length_106000_Anomaly...   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...  univariate/NormA/SinusRW_Length_108000_Anomaly...   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...  univariate/NormA/SinusRW_Length_116000_Anomaly...   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...  univariate/NormA/SinusRW_Length_120000_Anomaly...   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...  univariate/NormA/SinusRW_Length_124000_Anomaly...   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...  univariate/NormA/SinusRW_Length_148000_Anomaly...   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...  univariate/NormA/SinusRW_Length_196000_Anomaly...   \n",
       "\n",
       "                                                                   dataset_type  \\\n",
       "collection_name dataset_name                                                      \n",
       "NormA           Discords_annsgun                                           real   \n",
       "                Discords_dutch_power_demand                                real   \n",
       "                Discords_marotta_valve_tek_14                              real   \n",
       "                Discords_marotta_valve_tek_16                              real   \n",
       "                Discords_marotta_valve_tek_17                              real   \n",
       "                Discords_patient_respiration1                              real   \n",
       "                Discords_patient_respiration2                              real   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...    synthetic   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...    synthetic   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...    synthetic   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...    synthetic   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...    synthetic   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...    synthetic   \n",
       "\n",
       "                                                                    datetime_index  \\\n",
       "collection_name dataset_name                                                         \n",
       "NormA           Discords_annsgun                                             False   \n",
       "                Discords_dutch_power_demand                                  False   \n",
       "                Discords_marotta_valve_tek_14                                False   \n",
       "                Discords_marotta_valve_tek_16                                False   \n",
       "                Discords_marotta_valve_tek_17                                False   \n",
       "                Discords_patient_respiration1                                False   \n",
       "                Discords_patient_respiration2                                False   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...           False   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...           False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...           False   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...           False   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...           False   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...           False   \n",
       "\n",
       "                                                                    split_at  \\\n",
       "collection_name dataset_name                                                   \n",
       "NormA           Discords_annsgun                                         NaN   \n",
       "                Discords_dutch_power_demand                              NaN   \n",
       "                Discords_marotta_valve_tek_14                            NaN   \n",
       "                Discords_marotta_valve_tek_16                            NaN   \n",
       "                Discords_marotta_valve_tek_17                            NaN   \n",
       "                Discords_patient_respiration1                            NaN   \n",
       "                Discords_patient_respiration2                            NaN   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...       NaN   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...       NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...       NaN   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...       NaN   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...       NaN   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...       NaN   \n",
       "\n",
       "                                                                      train_type  \\\n",
       "collection_name dataset_name                                                       \n",
       "NormA           Discords_annsgun                                    unsupervised   \n",
       "                Discords_dutch_power_demand                         unsupervised   \n",
       "                Discords_marotta_valve_tek_14                       unsupervised   \n",
       "                Discords_marotta_valve_tek_16                       unsupervised   \n",
       "                Discords_marotta_valve_tek_17                       unsupervised   \n",
       "                Discords_patient_respiration1                       unsupervised   \n",
       "                Discords_patient_respiration2                       unsupervised   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...  unsupervised   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...  unsupervised   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...  unsupervised   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...  unsupervised   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...  unsupervised   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...  unsupervised   \n",
       "\n",
       "                                                                    train_is_normal  \\\n",
       "collection_name dataset_name                                                          \n",
       "NormA           Discords_annsgun                                              False   \n",
       "                Discords_dutch_power_demand                                   False   \n",
       "                Discords_marotta_valve_tek_14                                 False   \n",
       "                Discords_marotta_valve_tek_16                                 False   \n",
       "                Discords_marotta_valve_tek_17                                 False   \n",
       "                Discords_patient_respiration1                                 False   \n",
       "                Discords_patient_respiration2                                 False   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...            False   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...            False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...            False   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...            False   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...            False   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...            False   \n",
       "\n",
       "                                                                    input_type  \\\n",
       "collection_name dataset_name                                                     \n",
       "NormA           Discords_annsgun                                    univariate   \n",
       "                Discords_dutch_power_demand                         univariate   \n",
       "                Discords_marotta_valve_tek_14                       univariate   \n",
       "                Discords_marotta_valve_tek_16                       univariate   \n",
       "                Discords_marotta_valve_tek_17                       univariate   \n",
       "                Discords_patient_respiration1                       univariate   \n",
       "                Discords_patient_respiration2                       univariate   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...  univariate   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...  univariate   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...  univariate   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...  univariate   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...  univariate   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...  univariate   \n",
       "\n",
       "                                                                    length  \\\n",
       "collection_name dataset_name                                                 \n",
       "NormA           Discords_annsgun                                      2000   \n",
       "                Discords_dutch_power_demand                          35040   \n",
       "                Discords_marotta_valve_tek_14                         5000   \n",
       "                Discords_marotta_valve_tek_16                         5000   \n",
       "                Discords_marotta_valve_tek_17                         5000   \n",
       "                Discords_patient_respiration1                         6500   \n",
       "                Discords_patient_respiration2                         4000   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...  104000   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...  106000   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...  108000   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  112000   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  112000   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  112000   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  112000   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  112000   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  112000   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...  116000   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...  120000   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...  124000   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...  148000   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...  196000   \n",
       "\n",
       "                                                                    dimensions  \\\n",
       "collection_name dataset_name                                                     \n",
       "NormA           Discords_annsgun                                             1   \n",
       "                Discords_dutch_power_demand                                  1   \n",
       "                Discords_marotta_valve_tek_14                                1   \n",
       "                Discords_marotta_valve_tek_16                                1   \n",
       "                Discords_marotta_valve_tek_17                                1   \n",
       "                Discords_patient_respiration1                                1   \n",
       "                Discords_patient_respiration2                                1   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...           1   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...           1   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...           1   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...           1   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...           1   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...           1   \n",
       "\n",
       "                                                                    contamination  \\\n",
       "collection_name dataset_name                                                        \n",
       "NormA           Discords_annsgun                                         0.075000   \n",
       "                Discords_dutch_power_demand                              0.091324   \n",
       "                Discords_marotta_valve_tek_14                            0.051200   \n",
       "                Discords_marotta_valve_tek_16                            0.025600   \n",
       "                Discords_marotta_valve_tek_17                            0.025600   \n",
       "                Discords_patient_respiration1                            0.030769   \n",
       "                Discords_patient_respiration2                            0.075000   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...       0.038462   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...       0.056604   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...       0.074074   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       0.107143   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       0.107143   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       0.107143   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       0.107143   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       0.107143   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...       0.107143   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...       0.137931   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...       0.166667   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...       0.193548   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...       0.324324   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...       0.489796   \n",
       "\n",
       "                                                                    num_anomalies  \\\n",
       "collection_name dataset_name                                                        \n",
       "NormA           Discords_annsgun                                                1   \n",
       "                Discords_dutch_power_demand                                     4   \n",
       "                Discords_marotta_valve_tek_14                                   2   \n",
       "                Discords_marotta_valve_tek_16                                   1   \n",
       "                Discords_marotta_valve_tek_17                                   1   \n",
       "                Discords_patient_respiration1                                   2   \n",
       "                Discords_patient_respiration2                                   2   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...             20   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...             40   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...             80   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...            100   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...             60   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...             60   \n",
       "\n",
       "                                                                    min_anomaly_length  \\\n",
       "collection_name dataset_name                                                             \n",
       "NormA           Discords_annsgun                                                   150   \n",
       "                Discords_dutch_power_demand                                        800   \n",
       "                Discords_marotta_valve_tek_14                                      128   \n",
       "                Discords_marotta_valve_tek_16                                      128   \n",
       "                Discords_marotta_valve_tek_17                                      128   \n",
       "                Discords_patient_respiration1                                      100   \n",
       "                Discords_patient_respiration2                                      150   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...                 200   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...                 100   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...                 200   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...                 200   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...                 400   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...                 800   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...                1600   \n",
       "\n",
       "                                                                    median_anomaly_length  \\\n",
       "collection_name dataset_name                                                                \n",
       "NormA           Discords_annsgun                                                      150   \n",
       "                Discords_dutch_power_demand                                           800   \n",
       "                Discords_marotta_valve_tek_14                                         128   \n",
       "                Discords_marotta_valve_tek_16                                         128   \n",
       "                Discords_marotta_valve_tek_17                                         128   \n",
       "                Discords_patient_respiration1                                         100   \n",
       "                Discords_patient_respiration2                                         150   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...                    200   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...                    100   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...                    200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                    200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                    200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                    200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                    200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                    200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                    200   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...                    200   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...                    200   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...                    400   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...                    800   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...                   1600   \n",
       "\n",
       "                                                                    max_anomaly_length  \\\n",
       "collection_name dataset_name                                                             \n",
       "NormA           Discords_annsgun                                                   150   \n",
       "                Discords_dutch_power_demand                                        800   \n",
       "                Discords_marotta_valve_tek_14                                      128   \n",
       "                Discords_marotta_valve_tek_16                                      128   \n",
       "                Discords_marotta_valve_tek_17                                      128   \n",
       "                Discords_patient_respiration1                                      100   \n",
       "                Discords_patient_respiration2                                      150   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...                 200   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...                 100   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...                 200   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...                 200   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...                 200   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...                 400   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...                 800   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...                1600   \n",
       "\n",
       "                                                                            mean  \\\n",
       "collection_name dataset_name                                                       \n",
       "NormA           Discords_annsgun                                    3.097742e+02   \n",
       "                Discords_dutch_power_demand                         1.144038e+03   \n",
       "                Discords_marotta_valve_tek_14                       1.120064e+00   \n",
       "                Discords_marotta_valve_tek_16                       1.054768e+00   \n",
       "                Discords_marotta_valve_tek_17                       9.959120e-01   \n",
       "                Discords_patient_respiration1                       7.452095e+01   \n",
       "                Discords_patient_respiration2                       7.977460e+01   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_... -6.943642e-15   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_... -5.439808e-15   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_... -1.370558e-15   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  9.062465e-15   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  5.083426e-15   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_... -1.226194e-14   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_... -4.125208e-15   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_... -8.619898e-15   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_... -8.522453e-15   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_... -7.126989e-15   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100... -4.704740e-15   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...  3.163061e-15   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_... -7.988805e-17   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60... -2.561434e-15   \n",
       "\n",
       "                                                                        stddev  \\\n",
       "collection_name dataset_name                                                     \n",
       "NormA           Discords_annsgun                                     96.244237   \n",
       "                Discords_dutch_power_demand                         292.725791   \n",
       "                Discords_marotta_valve_tek_14                         1.703123   \n",
       "                Discords_marotta_valve_tek_16                         1.665258   \n",
       "                Discords_marotta_valve_tek_17                         1.626007   \n",
       "                Discords_patient_respiration1                       289.473775   \n",
       "                Discords_patient_respiration2                        77.999132   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...    1.413004   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...    1.414737   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...    1.412455   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    1.413815   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    1.411193   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    1.412934   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    1.413281   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    1.410894   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...    1.411797   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...    1.413798   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...    1.413511   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...    1.414285   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...    1.414400   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...    1.411909   \n",
       "\n",
       "                                                                       trend  \\\n",
       "collection_name dataset_name                                                   \n",
       "NormA           Discords_annsgun                                    no trend   \n",
       "                Discords_dutch_power_demand                         no trend   \n",
       "                Discords_marotta_valve_tek_14                       no trend   \n",
       "                Discords_marotta_valve_tek_16                       no trend   \n",
       "                Discords_marotta_valve_tek_17                       no trend   \n",
       "                Discords_patient_respiration1                       no trend   \n",
       "                Discords_patient_respiration2                       no trend   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...  no trend   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...  no trend   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...  no trend   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...  no trend   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...  no trend   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...  no trend   \n",
       "\n",
       "                                                                             stationarity  \\\n",
       "collection_name dataset_name                                                                \n",
       "NormA           Discords_annsgun                                    difference_stationary   \n",
       "                Discords_dutch_power_demand                                    stationary   \n",
       "                Discords_marotta_valve_tek_14                       difference_stationary   \n",
       "                Discords_marotta_valve_tek_16                       difference_stationary   \n",
       "                Discords_marotta_valve_tek_17                       difference_stationary   \n",
       "                Discords_patient_respiration1                                  stationary   \n",
       "                Discords_patient_respiration2                                  stationary   \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...             stationary   \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...             stationary   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...             stationary   \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...             stationary   \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...             stationary   \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...             stationary   \n",
       "\n",
       "                                                                    period_size  \n",
       "collection_name dataset_name                                                     \n",
       "NormA           Discords_annsgun                                            NaN  \n",
       "                Discords_dutch_power_demand                                 NaN  \n",
       "                Discords_marotta_valve_tek_14                               NaN  \n",
       "                Discords_marotta_valve_tek_16                               NaN  \n",
       "                Discords_marotta_valve_tek_17                               NaN  \n",
       "                Discords_patient_respiration1                               NaN  \n",
       "                Discords_patient_respiration2                               NaN  \n",
       "                SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_...          NaN  \n",
       "                SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_...          NaN  \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_...          NaN  \n",
       "                SinusRW_Length_120000_AnomalyL_200_AnomalyN_100...          NaN  \n",
       "                SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_...          NaN  \n",
       "                SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60...          NaN  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>domain</th>\n",
       "      <th>anomalies</th>\n",
       "      <th>anomaly_indices</th>\n",
       "      <th>anomaly_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Discords_annsgun</th>\n",
       "      <td>2000</td>\n",
       "      <td>Video recording</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_marotta_valve_tek_16</th>\n",
       "      <td>5000</td>\n",
       "      <td>Space Engineering</td>\n",
       "      <td>1</td>\n",
       "      <td>4400</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_marotta_valve_tek_17</th>\n",
       "      <td>5000</td>\n",
       "      <td>Space Engineering</td>\n",
       "      <td>1</td>\n",
       "      <td>2100</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_marotta_valve_tek_14</th>\n",
       "      <td>5000</td>\n",
       "      <td>Space Engineering</td>\n",
       "      <td>2</td>\n",
       "      <td>1100;1500</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_patient_respiration1</th>\n",
       "      <td>6500</td>\n",
       "      <td>Pneumology</td>\n",
       "      <td>1</td>\n",
       "      <td>800;5000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_patient_respiration2</th>\n",
       "      <td>4000</td>\n",
       "      <td>Pneumology</td>\n",
       "      <td>1</td>\n",
       "      <td>3000;3400</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discords_dutch_power_demand</th>\n",
       "      <td>35040</td>\n",
       "      <td>Energy consumption study</td>\n",
       "      <td>4</td>\n",
       "      <td>11453;33993;7786;12639</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               length                    domain  anomalies  \\\n",
       "dataset                                                                      \n",
       "Discords_annsgun                 2000           Video recording          1   \n",
       "Discords_marotta_valve_tek_16    5000         Space Engineering          1   \n",
       "Discords_marotta_valve_tek_17    5000         Space Engineering          1   \n",
       "Discords_marotta_valve_tek_14    5000         Space Engineering          2   \n",
       "Discords_patient_respiration1    6500                Pneumology          1   \n",
       "Discords_patient_respiration2    4000                Pneumology          1   \n",
       "Discords_dutch_power_demand     35040  Energy consumption study          4   \n",
       "\n",
       "                                      anomaly_indices  anomaly_length  \n",
       "dataset                                                                \n",
       "Discords_annsgun                                  300             150  \n",
       "Discords_marotta_valve_tek_16                    4400             128  \n",
       "Discords_marotta_valve_tek_17                    2100             128  \n",
       "Discords_marotta_valve_tek_14               1100;1500             128  \n",
       "Discords_patient_respiration1                800;5000             100  \n",
       "Discords_patient_respiration2               3000;3400             150  \n",
       "Discords_dutch_power_demand    11453;33993;7786;12639             800  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANOMALY_INFORMATION = \"anomaly_information.csv\"\n",
    "df_anomaly = pd.read_csv(source_folder / ANOMALY_INFORMATION)\n",
    "df_anomaly = df_anomaly.set_index(\"dataset\")\n",
    "df_anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_series_paths = [f for f in source_folder.glob(\"*.ts\") if f.stem != ANOMALY_INFORMATION.split(\".\")[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset_name='Discords_annsgun'\n",
      "dataset_type='real'\n",
      "discord_positions=array([300]), discord_length=150\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196.37467</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196.49124</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196.79989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>197.17570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>197.87943</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>266.45147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>269.02330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>271.33349</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>273.94659</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>276.41270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               value  is_anomaly\n",
       "timestamp                       \n",
       "0          196.37467           0\n",
       "1          196.49124           0\n",
       "2          196.79989           0\n",
       "3          197.17570           0\n",
       "4          197.87943           0\n",
       "...              ...         ...\n",
       "1995       266.45147           0\n",
       "1996       269.02330           0\n",
       "1997       271.33349           0\n",
       "1998       273.94659           0\n",
       "1999       276.41270           0\n",
       "\n",
       "[2000 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = time_series_paths[-8]\n",
    "dataset_name = filepath.stem\n",
    "print(f\"{dataset_name=}\")\n",
    "\n",
    "dataset_type = \"real\"\n",
    "if dataset_name.startswith(\"SinusRW\"):\n",
    "    dataset_type = \"synthetic\"\n",
    "print(f\"{dataset_type=}\")\n",
    "\n",
    "if dataset_type == \"synthetic\":\n",
    "    discord_positions = np.genfromtxt(filepath.parent / f\"{dataset_name}_Annotations.txt\", delimiter=\",\")\n",
    "    discord_length = int(filepath.stem.split(\"_\")[4].strip())\n",
    "    length = None\n",
    "else:\n",
    "    discord_positions = df_anomaly.loc[dataset_name, \"anomaly_indices\"].split(\";\")\n",
    "    discord_positions = np.array([int(i) for i in discord_positions])\n",
    "    discord_length = df_anomaly.loc[dataset_name, \"anomaly_length\"]\n",
    "    length = df_anomaly.loc[dataset_name, \"length\"]\n",
    "print(f\"{discord_positions=}, {discord_length=}\")\n",
    "anomalies = []\n",
    "for idx in discord_positions:\n",
    "    anomalies.append(np.arange(idx, idx+discord_length))\n",
    "\n",
    "df = pd.read_csv(filepath, header=None)\n",
    "if length is not None:\n",
    "    df = df.iloc[:length]\n",
    "\n",
    "df.index.name = \"timestamp\"\n",
    "df.columns = [\"value\"]\n",
    "\n",
    "# insert labels\n",
    "df[\"is_anomaly\"] = 0\n",
    "for anomaly in anomalies:\n",
    "    df.loc[anomaly, \"is_anomaly\"] = 1\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df.iloc[:, :-1]\n",
    "df_tmp.plot()\n",
    "s = df[\"is_anomaly\"].diff()\n",
    "y_max = df_tmp.max().max()\n",
    "y_min = df_tmp.min().min()\n",
    "for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((begin, y_min), end - begin, y_max - y_min, color=\"red\", alpha=0.25))\n",
    "#plt.gca().set_xlim(2500, 4500)\n",
    "#plt.savefig(\"SinusRW-clip.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset=PosixPath('/home/projects/akita/data/benchmark-data/data-processed/univariate/NormA/Discords_dutch_power_demand.test.csv')\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dataset = target_folder / dataset_subfolder / \"Discords_marotta_valve_tek_16.test.csv\"\n",
    "#dataset = target_folder / dataset_subfolder / \"Discords_marotta_valve_tek_17.test.csv\"\n",
    "#dataset = target_folder / dataset_subfolder / \"Discords_marotta_valve_tek_14.test.csv\"\n",
    "#dataset = target_folder / dataset_subfolder / \"Discords_patient_respiration2.test.csv\"\n",
    "dataset = target_folder / dataset_subfolder / \"Discords_dutch_power_demand.test.csv\"\n",
    "#dataset = target_folder / dataset_subfolder / \"Discords_annsgun.test.csv\"\n",
    "print(f\"{dataset=}\")\n",
    "df_tmp = pd.read_csv(dataset)\n",
    "df_tmp.iloc[:, 1].plot()\n",
    "s = df_tmp[\"is_anomaly\"].diff()\n",
    "y_max = df_tmp.max().max()\n",
    "y_min = df_tmp.min().min()\n",
    "for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((begin, y_min), end - begin, y_max - y_min, color=\"red\", alpha=0.25))\n",
    "#plt.gca().set_xlim(2000, 6000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
